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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX)
Sri Kusuma Aditya, Christian
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Comparison of Classification for Indonesian Language News Documents Using Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) Algorithms Sri Kusuma Aditya, Christian; Ridha Agam, Muh; Rezky Fadillah, Andhika; Setio Wiyono, Briansyah
Informatics and Digital Expert (INDEX) Vol. 6 No. 2 (2024): INDEX, November 2024
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v6i2.1888

Abstract

The development of online news has grown very fast. The high volume of text documents was triggered by activities from various news sources. Due to the large amount of news that is included on the website, sometimes the news is posted not according to its category which is most likely caused by human error. The grouping of online news is important for user convenience in searching for news according to its category. It need an intelligent system that can classify online news automatically. This research evaluates deep learning techniques using LSTM and RNN, and compared with the results obtained from previous studies, which used the NBC algorithm. To experiment the system, an Indonesia News Corpus with 7 different categories and total 2100 documents, collected by crawling online national news portals, is used. Due to the unbalanced number of class compositions or news categories, integration is also carried out SMOTE. The average empirical results show that the classification accuracy from RNN with SMOTE with an accuracy of 95.2% and followed by LSTM with SMOTE is 97.8%, both of which are able to outperform the NBC method with an accuracy of 73.2%.
Combination of Term Weighting with Class Distribution and Centroid-based Approach for Document Classification Sri Kusuma Aditya, Christian; Sumadi, Fauzi Dwi Setiawan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 4, November 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i4`.1793

Abstract

A text retrieval system requires a method that is able to return a number of documents with high relevance upon user requests. One of the important stages in the text representation process is the weighting process. The use of Term Frequency (TF) considers the number of word occurrences in each document, while Inverse Document Frequency (IDF) considers the wide distribution of words throughout the document collection. However, the TF-IDF weighting cannot represent the distribution of words to documents with many classes or categories. The more unequal the distribution of words in each category, the more important the word features should be. This study developed a new term weighting method where weighting is carried out based on the frequency of occurrence of terms in each class which is integrated with the distribution of centroid-based terms which can minimize intra-cluster similarity and maximize inter-cluster variance. The ICF.TDCB term weighting method has been able to provide the best results in its application to SVM modeling with a dataset of 931 online news documents. The results show that SVM modeling had accuracy of 0.723, outperforming the use of other term weightings such as TF.IDF, ICF & TDCB.
CATARACT CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) INCEPTION RESNETV2 Zulfa, M. Mauludin; Sri Kusuma Aditya, Christian
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2340

Abstract

The eye is a human sensory device that functions as an organ of vision. Referring to data from the World Health Organization (WHO) in 2018, cataracts are responsible for 48% of blindness cases in the world and are the main cause in Indonesia. People still find it difficult to distinguish cataract eyes from normal eyes, so they often do not realize the indications of cataract disease. It is important to conduct early detection of cataract disease before blindness occurs. As technology develops, cataract identification becomes easier and simpler with digital image processing classification. This research develops a cataract image classification model using Convolutional Neural Network (CNN) with Inception-ResnetV2 architecture to identify cataract eyes with normal eyes. The proposed model consists of two parts of Inception-ResnetV2 architecture as the base model, and the head model in the form of Fully Connected Layers consisting of global average polling, 2 dense relu layers of 128 and 256 neurons, 2 batch normalization layers, 2 layers of dropout parameter 0.5, and softmax activation function for the output layer. To improve model training, the Stochastic Gradient Descent (SGD) optimization function is used. The dataset consists of 2,192 eye fundus images with 2 main classes of cataract and normal taken from the public data provider site Kaggle. Learning rate tests on the optimization function were carried out with parameters 0.1, 0.01, and 0.001, the results of the proposed model compiled with Stochastic Gradient Descent (SGD) learning rate 0.01 gave a final accuracy of 96%.
Comparison of Classification for Indonesian Language News Documents Using Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) Algorithms Sri Kusuma Aditya, Christian; Ridha Agam, Muh; Rezky Fadillah, Andhika; Setio Wiyono, Briansyah
Informatics and Digital Expert (INDEX) Vol. 6 No. 2 (2024): INDEX, November 2024
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v6i2.1888

Abstract

The development of online news has grown very fast. The high volume of text documents was triggered by activities from various news sources. Due to the large amount of news that is included on the website, sometimes the news is posted not according to its category which is most likely caused by human error. The grouping of online news is important for user convenience in searching for news according to its category. It need an intelligent system that can classify online news automatically. This research evaluates deep learning techniques using LSTM and RNN, and compared with the results obtained from previous studies, which used the NBC algorithm. To experiment the system, an Indonesia News Corpus with 7 different categories and total 2100 documents, collected by crawling online national news portals, is used. Due to the unbalanced number of class compositions or news categories, integration is also carried out SMOTE. The average empirical results show that the classification accuracy from RNN with SMOTE with an accuracy of 95.2% and followed by LSTM with SMOTE is 97.8%, both of which are able to outperform the NBC method with an accuracy of 73.2%.
Co-Authors Rezky Fadillah, Andhika Ridha Agam, Muh Setio Wiyono, Briansyah Sumadi, Fauzi Dwi Setiawan Zulfa, M. Mauludin